In recent, technologies relating to big data have been spotlighted. This is attributed to the circumstances that tremendous amounts of data are being globally accumulated with the growth of various types of electronic commerce or social networks, and utilizing more data enables higher quality analysis of data.
Of the technologies, a skyline query is a data analysis technique that can help decision making of users and companies.
For example, by analyzing data such as user's product purchase information, it is possible to recommend, to the user who intends to purchase a new product, his/her personalized product [ex): utilizing a skyline query or a dynamic skyline query], or when a company makes a new product, it is possible to predict effects of the product [ex): utilizing a reverse skyline query]. The skyline query, the dynamic skyline query, and the reverse skyline query are specifically described later with reference to FIG. 1 to FIG. 3.
For the foregoing reasons, it is desirable to utilize as many data as possible upon skyline calculation. However, conventional skyline calculation techniques cannot easily deal with the big data.
For example, in order to implement skyline calculation, various methods such as BNL, D&Q SFS, Bitmap, NN, and BBS have been proposed, but are problematic in that since they cannot accomplish parallelization using multiple computers, they require significantly long time for a big data or a significantly large memory space. In order to overcome the drawbacks, algorithms such as MRBNL and PPPS, which calculate skylines in parallel by using multiple computers or multi-core processors, have been suggested, but are still problematic in that they do not make the most of the parallelization in implementing the calculation.
Accordingly, an effective skyline query processing system and method, which can effectively process big data in parallel, are necessary. Also, a skyline query processing system and method, which can easily extend parallel processing for a skyline query to parallel processing for a dynamic skyline query or a reverse skyline query, are necessary.
With respect to the example embodiments, Korean Patent Registration No. 10-0976132 (“Method for Searching Personalized Top K Skylines by Using User Preference Information and a Computer Readable Record Medium thereof”) describes searching top K skylines by determining priority between subset of qualitative preference of a user and a sky cube.
In addition, U.S. Pat. No. 8,504,581 B2 (“Multiple Criteria Decision Analysis”) describes implementing skyline calculation based on join calculation of a relational database.